Quantification and visualization of coordination during non-cyclic upper extremity motion

Abstract

There are many design challenges in creating at-home tele-monitoring systems that enable quantification and visualization of complex biomechanical behavior. One such challenge is robustly quantifying joint coordination in a way that is intuitive and supports clinical decision-making. This work defines a new measure of coordination called the relative coordination metric (RCM) and its accompanying normalization schemes. RCM enables quantification of coordination during non-constrained discrete motions. Here RCM is applied to a grasping task. Fifteen healthy participants performed a reach, grasp, transport, and release task with a cup and a pen. The measured joint angles were then time-normalized and the RCM time-series were calculated between the shoulder-elbow, shoulder-wrist, and elbow-wrist. RCM was normalized using four differing criteria: the selected joint degree of freedom, angular velocity, angular magnitude, and range of motion. Percent time spent in specified RCM ranges was used asa composite metric and was evaluated for each trial. RCM was found to vary based on: (1) chosen normalization scheme, (2) the stage within the task, (3) the object grasped, and (4) the trajectory of the motion. The RCM addresses some of the limitations of current measures of coordination because it is applicable to discrete motions, does not rely on cyclic repetition, and uses velocity-based measures. Future work will explore clinically relevant differences in the RCM as it is expanded to evaluate different tasks and patient populations.

A Wearable-Sensor System with AI Technology for Real-Time Biomechanical Feedback Training in Hammer Throw

“…In most sports, limb coordination is an essential factor influencing the performance of a motor skill, but it is very difficult to be quantified [ 1 , 2 , 3 ]. Obtaining reliable kinematic and kinetic parameters, such as joint angles, applied forces, etc., and further performing 3D motion analysis can help to reveal the related hidden information.…”

Path Planning and Impedance Control of a Soft Modular Exoskeleton for Coordinated Upper Limb Rehabilitation

“…Thus, joint coordination must be investigated. In previous studies, principal component analysis (Tang et al, 2019 ), artificial neural networks, and other methods (Fineman and Stirling, 2017 ) have been used to quantify the coordination of upper limb joints. With coordination quantification, exoskeletons can be controlled to assist human limbs by following the coordination law.…”